Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
XML PDF e-pub
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Bibliography

Major publications by the team in recent years
[1]
O. Abdelkafi, L. Idoumghar, J. Lepagnot.
A Survey on the Metaheuristics Applied to QAP for the Graphics Processing Units, in: Parallel Processing Letters, 2016, vol. 26, no 3, pp. 1–20.
[2]
A. Bendjoudi, N. Melab, E. Talbi.
FTH-B&B: A Fault-Tolerant HierarchicalBranch and Bound for Large ScaleUnreliable Environments, in: IEEE Trans. Computers, 2014, vol. 63, no 9, pp. 2302–2315.
[3]
S. Cahon, N. Melab, E. Talbi.
ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics, in: J. Heuristics, 2004, vol. 10, no 3, pp. 357–380.
[4]
F. Daolio, A. Liefooghe, S. Verel, H. Aguirre, K. Tanaka.
Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes, in: Evolutionary Computation, 2017, vol. 25, no 4.
[5]
B. Derbel.
Contributions to single- and multi- objective optimization: towards distributed and autonomous massive optimization, Université de Lille, 2017, HDR dissertation.
[6]
B. Derbel, A. Liefooghe, Q. Zhang, H. Aguirre, K. Tanaka.
Multi-objective Local Search Based on Decomposition, in: Parallel Problem Solving from Nature - PPSN XIV - 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings, 2016, pp. 431–441.
[7]
J. Gmys, M. Mezmaz, N. Melab, D. Tuyttens.
IVM-based parallel branch-and-bound using hierarchical work stealing on multi-GPU systems, in: Concurrency and Computation: Practice and Experience, 2017, vol. 29, no 9.
[8]
A. Liefooghe, B. Derbel, S. Verel, H. Aguirre, K. Tanaka.
Towards Landscape-Aware Automatic Algorithm Configuration: Preliminary Experiments on Neutral and Rugged Landscapes, in: Evolutionary Computation in Combinatorial Optimization - 17th European Conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, 2017, pp. 215–232.
[9]
T. V. Luong, N. Melab, E. Talbi.
GPU Computing for Parallel Local Search Metaheuristic Algorithms, in: IEEE Trans. Computers, 2013, vol. 62, no 1, pp. 173–185.
[10]
A. Nakib, S. Ouchraa, N. Shvai, L. Souquet, E. Talbi.
Deterministic metaheuristic based on fractal decomposition for large-scale optimization, in: Appl. Soft Comput., 2017, vol. 61, pp. 468–485.
Publications of the year

Articles in International Peer-Reviewed Journals

[11]
J. S. Almeida, P. P. Rebouças Filho, T. Carneiro, W. Wei, R. Damaševičius, R. Maskeliūnas, V. H. C. de Albuquerque.
Detecting Parkinson's Disease with Sustained Phonation and Speech Signals using Machine Learning Techniques, in: Pattern Recognition Letters, July 2019, vol. 125, pp. 55-62. [ DOI : 10.1016/j.patrec.2019.04.005 ]
https://hal.archives-ouvertes.fr/hal-02380596
[12]
L. Asli, M. Aïder, E.-G. Talbi.
Solving a dynamic combinatorial auctions problem by a hybrid metaheuristic based on a fuzzy dominance relation, in: RAIRO - Operations Research, January 2019, vol. 53, no 1, pp. 207-221. [ DOI : 10.1051/ro/2018051 ]
https://hal.archives-ouvertes.fr/hal-02304722
[13]
T. Carneiro, J. Gmys, N. Melab, D. Tuyttens.
Towards ultra-scale Branch-and-Bound using a high-productivity language, in: Future Generation Computer Systems, November 2019. [ DOI : 10.1016/j.future.2019.11.011 ]
https://hal.archives-ouvertes.fr/hal-02371238
[14]
N. Dupin, E.-G. Talbi.
Parallel matheuristics for the discrete unit commitment problem with min-stop ramping constraints, in: International Transactions in Operational Research, January 2020, vol. 27, no 1, pp. 219-244. [ DOI : 10.1111/itor.12557 ]
https://hal.archives-ouvertes.fr/hal-02304758
[15]
J. Gmys, M. Mezmaz, N. Melab, D. Tuyttens.
A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem, in: European Journal of Operational Research, 2020, forthcoming.
https://hal.inria.fr/hal-02421229
[16]
A. Liefooghe, F. Daolio, S. Verel, B. Derbel, H. Aguirre, K. Tanaka.
Landscape-aware performance prediction for evolutionary multi-objective optimization, in: IEEE Transactions on Evolutionary Computation, 2019, forthcoming. [ DOI : 10.1109/TEVC.2019.2940828 ]
https://hal.archives-ouvertes.fr/hal-02294201
[17]
A. Nakib, L. Souquet, E.-G. Talbi.
Parallel fractal decomposition based algorithm for big continuous optimization problems, in: Journal of Parallel and Distributed Computing, November 2019, vol. 133, pp. 297-306. [ DOI : 10.1016/j.jpdc.2018.06.002 ]
https://hal.archives-ouvertes.fr/hal-02304882
[18]
J. Pelamatti, L. Brévault, M. Balesdent, E.-G. Talbi, Y. Guerin.
Efficient global optimization of constrained mixed variable problems, in: Journal of Global Optimization, March 2019, vol. 73, no 3, pp. 583-613. [ DOI : 10.1007/s10898-018-0715-1 ]
https://hal.archives-ouvertes.fr/hal-02304730
[19]
O. Schutze, C. Hernandez, E.-G. Talbi, J.-Q. Sun, Y. Naranjani, F.-R. Xiong.
Archivers for the representation of the set of approximate solutions for MOPs, in: Journal of Heuristics, February 2019, vol. 25, no 1, pp. 71-105. [ DOI : 10.1007/s10732-018-9383-z ]
https://hal.archives-ouvertes.fr/hal-02304717
[20]
E.-G. Talbi.
A unified view of parallel multi-objective evolutionary algorithms, in: Journal of Parallel and Distributed Computing, November 2019, vol. 133, pp. 349-358. [ DOI : 10.1016/j.jpdc.2018.04.012 ]
https://hal.archives-ouvertes.fr/hal-02304734
[21]
A. Tchernykh, U. Schwiegelsohn, E.-G. Talbi, M. Babenko.
Towards understanding uncertainty in cloud computing with risks of confidentiality, integrity, and availability, in: Journal of computational science, September 2019, vol. 36, 100581 p. [ DOI : 10.1016/j.jocs.2016.11.011 ]
https://hal.archives-ouvertes.fr/hal-02304771

International Conferences with Proceedings

[22]
O. Abdelkafi, B. Derbel, A. Liefooghe.
A Parallel Tabu Search for the Large-scale Quadratic Assignment Problem, in: IEEE CEC 2019 - IEEE Congress on Evolutionary Computation, Wellington, New Zealand, June 2019.
https://hal.archives-ouvertes.fr/hal-02179193
[23]
N. Berveglieri, B. Derbel, A. Liefooghe, H. Aguirre, K. Tanaka.
Surrogate-assisted multiobjective optimization based on decomposition, in: GECCO '19 - Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czech Republic, ACM Press, July 2019, pp. 507-515. [ DOI : 10.1145/3321707.3321836 ]
https://hal.archives-ouvertes.fr/hal-02292851
[24]
T. Carneiro, N. Melab.
An Incremental Parallel PGAS-based Tree Search Algorithm, in: HPCS 2019 - International Conference on High Performance Computing & Simulation, Dublin, Ireland, July 2019.
https://hal.archives-ouvertes.fr/hal-02170842
[25]
T. Carneiro, N. Melab.
Productivity-aware Design and Implementation of Distributed Tree-based Search Algorithms, in: ICCS 2019 - International Conference on Computational Science, Faro, Portugal, June 2019.
https://hal.archives-ouvertes.fr/hal-02139177
[26]
B. Derbel, A. Liefooghe, S. Verel, H. Aguirre, K. Tanaka.
New Features for Continuous Exploratory Landscape Analysis based on the SOO Tree, in: FOGA 2019 - 15th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms, Potsdam, Germany, ACM Press, August 2019, pp. 72-86.
https://hal.inria.fr/hal-02282986
[27]
M. Gobert, J. Gmys, J.-F. Toubeau, F. Vallee, N. Melab, D. Tuyttens.
Surrogate-Assisted Optimization for Multi-stage Optimal Scheduling of Virtual Power Plants, in: PaCOS 2019 - International Workshop on the Synergy of Parallel Computing, Optimization and Simulation (part of HPCS 2019), Dublin, Ireland, July 2019.
https://hal.inria.fr/hal-02178314
[28]
T. Ito, H. Aguirre, K. Tanaka, A. Liefooghe, B. Derbel, S. Verel.
Estimating Relevance of Variables for Effective Recombination, in: EMO 2019 - International Conference on Evolutionary Multi-Criterion Optimization, East Lansing, Michigan, United States, February 2019, pp. 411-423. [ DOI : 10.1007/978-3-030-12598-1_33 ]
https://hal.archives-ouvertes.fr/hal-02064547
[29]
Best Paper
Y. Marca, H. Aguirre, S. Z. Martinez, A. Liefooghe, B. Derbel, S. Verel, K. Tanaka.
Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective Problems, in: EMO 2019 - International Conference on Evolutionary Multi-Criterion Optimization, East Lansing, Michigan, United States, February 2019, pp. 386-398. [ DOI : 10.1007/978-3-030-12598-1_31 ]
https://hal.archives-ouvertes.fr/hal-02064548
[30]
H. Monzón, H. Aguirre, S. Verel, A. Liefooghe, B. Derbel, K. Tanaka.
Dynamic compartmental models for algorithm analysis and population size estimation, in: Genetic and Evolutionary Computation Conference Companion, Prague, Czech Republic, Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19), ACM Press, July 2019, pp. 2044-2047. [ DOI : 10.1145/3319619.3326912 ]
https://hal.archives-ouvertes.fr/hal-02436226
[31]
J. Pelamatti, L. Brévault, M. Balesdent, E.-G. Talbi, Y. Guerin.
Surrogate model based optimization of constrained mixed variable problems: application to the design of a launch vehicle thrust frame, in: SciTech 2019 - AIAA Science and Technology Forum and Exposition, San Diego, United States, American Institute of Aeronautics and Astronautics, January 2019. [ DOI : 10.2514/6.2019-1971 ]
https://hal.archives-ouvertes.fr/hal-02304816
[32]
L. Souquet, A. Nakib, E.-G. Talbi.
Deterministic multi-objective fractal decomposition algorithm, in: MIC 2019 - 13th Metaheuristics International Conference, Cartagena, Colombia, July 2019.
https://hal.archives-ouvertes.fr/hal-02304975

National Conferences with Proceedings

[33]
D. Delabroye, S. Delamare, D. Loup, L. Nussbaum.
Remplacer un routeur par un serveur Linux : retour d'expérience des passerelles d'accès à Grid'5000, in: JRES - Journées Réseaux de l'Enseignement et de la Recherche, Dijon, France, December 2019.
https://hal.inria.fr/hal-02401684

Scientific Books (or Scientific Book chapters)

[34]
T. Bartz-Beielstein, B. Filipič, P. Korošec, E.-G. Talbi.
High-Performance Simulation-Based Optimization, Springer, 2020. [ DOI : 10.1007/978-3-030-18764-4 ]
https://hal.archives-ouvertes.fr/hal-02304686
[35]
N. Dupin, F. Nielsen, E.-G. Talbi.
K-Medoids Clustering Is Solvable in Polynomial Time for a 2d Pareto Front, in: Optimization of Complex Systems: Theory, Models, Algorithms and Applications, Springer, June 2020, pp. 790-799. [ DOI : 10.1007/978-3-030-21803-4_79 ]
https://hal.archives-ouvertes.fr/hal-02304806
[36]
A. Liefooghe, L. Paquete.
Proceedings of the 19th European conference on evolutionary computation in combinatorial optimization (EvoCOP 2019), Lecture Notes in Computer Science, Springer, 2019, vol. 11452. [ DOI : 10.1007/978-3-030-16711-0 ]
https://hal.archives-ouvertes.fr/hal-02292912
[37]
N. Melab, J. Gmys, M. Mezmaz, D. Tuyttens.
Many-core Branch-and-Bound for GPU accelerators and MIC coprocessors, in: High-Performance Simulation-Based Optimization, T. Bartz-Beielstein, B. Filipič, P. Korošec, E.-G. Talbi (editors), Studies in Computational Intelligence, Springer, June 2019, vol. 833, 16 p.
https://hal.inria.fr/hal-01924766
[38]
J. Pelamatti, L. Brévault, M. Balesdent, E.-G. Talbi, Y. Guerin.
Overview and Comparison of Gaussian Process-Based Surrogate Models for Mixed Continuous and Discrete Variables: Application on Aerospace Design Problems, in: High-Performance Simulation-Based Optimization, Springer, June 2020, pp. 189-224. [ DOI : 10.1007/978-3-030-18764-4_9 ]
https://hal.archives-ouvertes.fr/hal-02304707
References in notes
[39]
M. Balesdent, L. Brévault, N. B. Price, S. Defoort, R. Le Riche, N.-H. Kim, R. T. Haftka, N. Bérend.
Advanced Space Vehicle Design Taking into Account Multidisciplinary Couplings and Mixed Epistemic/Aleatory Uncertainties, in: Space Engineering: Modeling and Optimization with Case Studies, G. Fasano, J. D. Pintér (editors), Springer International Publishing, 2016, pp. 1–48.
http://dx.doi.org/10.1007/978-3-319-41508-6_1
[40]
B. Derbel, D. Brockhoff, A. Liefooghe, S. Verel.
On the Impact of Multiobjective Scalarizing Functions, in: Parallel Problem Solving from Nature - PPSN XIII - 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014. Proceedings, 2014, pp. 548–558.
[41]
B. Derbel, A. Liefooghe, G. Marquet, E. Talbi.
A fine-grained message passing MOEA/D, in: IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, 2015, pp. 1837–1844.
[42]
R. Haftka, D. Villanueva, A. Chaudhuri.
Parallel surrogate-assisted global optimization with expensive functions – a survey, in: Structural and Multidisciplinary Optimization, 2016, vol. 54(1), pp. 3–13.
[43]
D. Jones, M. Schonlau, W. Welch.
Efficient Global Optimization of Expensive Black-Box Functions, in: Journal of Global Optimization, 1998, vol. 13(4), pp. 455–492.
[44]
J. Pelamatti, L. Brevault, M. Balesdent, E.-G. Talbi, Y. Guerin.
How to deal with mixed-variable optimization problems: An overview of algorithms and formulations, in: Advances in Structural and Multidisciplinary Optimization, Proc. of the 12th World Congress of Structural and Multidisciplinary Optimization (WCSMO12), Springer, 2018, pp. 64–82.
http://dx.doi.org/10.1007/978-3-319-67988-4_5
[45]
F. Shahzad, J. Thies, M. Kreutzer, T. Zeiser, G. Hager, G. Wellein.
CRAFT: A library for easier application-level Checkpoint/Restart and Automatic Fault Tolerance, in: CoRR, 2017, vol. abs/1708.02030.
http://arxiv.org/abs/1708.02030
[46]
N. Shavit.
Data Structures in the Multicore Age, in: Communications of the ACM, 2011, vol. 54, no 3, pp. 76–84.
[47]
M. Snir, al..
Addressing Failures in Exascale Computing, in: Int. J. High Perform. Comput. Appl., May 2014, vol. 28, no 2, pp. 129–173.
[48]
E.-G. Talbi.
Combining metaheuristics with mathematical programming, constraint programming and machine learning, in: Annals OR, 2016, vol. 240, no 1, pp. 171–215.
[49]
T. Vu, B. Derbel.
Parallel Branch-and-Bound in multi-core multi-CPU multi-GPU heterogeneous environments, in: Future Generation Comp. Syst., 2016, vol. 56, pp. 95–109.
[50]
X. Zhang, Y. Tian, R. Cheng, Y. Jin.
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization, in: IEEE Trans. Evol. Computation, 2018, vol. 22, no 1, pp. 97–112.